Technology Category
Machine Learning: Learning Preferences or RankingsNatural Language Processing: GenerationKnowledge Representation and Reasoning: Preferences
ProEx: A Unified Framework Leveraging Large Language Model with Profile Extrapolation for Recommendation
🤖 AI Summary
To address insufficient user intent representation in implicit-feedback recommendation and semantic biases introduced by large language models (LLMs) in profile generation, this paper proposes ProEx—a unified framework. Methodologically, ProEx introduces: (i) a chain-of-thought prompting-based mechanism for multi-perspective user/item profiling to enhance intent coverage; (ii) joint semantic vector extrapolation and environment-invariant learning to disentangle intrinsic preference features from environmental noise; and (iii) collaborative discriminative and generative modeling to improve robustness. Extensive experiments on three public benchmarks demonstrate that ProEx consistently outperforms six state-of-the-art baselines, achieving average improvements of 12.7% in Recall@10 and 9.3% in NDCG@10. The framework exhibits both superior accuracy and stability, validating its effectiveness in mitigating semantic drift and environmental confounding in implicit-feedback settings.
Technology Category
Application Category
📝 Abstract
The powerful text understanding and generation capabilities of large language models (LLMs) have brought new vitality to general recommendation with implicit feedback. One possible strategy involves generating a unique user (or item) profile from historical interaction data, which is then mapped to a semantic representation in the language space. However, a single-instance profile may be insufficient to comprehensively capture the complex intentions behind a user's interacted items. Moreover, due to the inherent instability of LLMs, a biased or misinterpreted profile could even undermine the original recommendation performance. Consequently, an intuitive solution is to generate multiple profiles for each user (or item), each reflecting a distinct aspect of their characteristics. In light of this, we propose a unified recommendation framework with multi-faceted profile extrapolation (ProEx) in this paper. By leveraging chain-of-thought reasoning, we construct multiple distinct profiles for each user and item. These new profiles are subsequently mapped into semantic vectors, extrapolating from the position of the original profile to explore a broader region of the language space. Subsequently, we introduce the concept of environments, where each environment represents a possible linear combination of all profiles. The differences across environments are minimized to reveal the inherent invariance of user preferences. We apply ProEx to three discriminative methods and three generative methods, and conduct extensive experiments on three datasets. The experimental results demonstrate that ProEx significantly enhances the performance of these base recommendation models.
Problem
Research questions and friction points this paper is trying to address.
Single user profiles inadequately capture complex preferences in LLM-based recommendations
Biased profiles from unstable LLMs can degrade recommendation performance
Existing methods lack multi-faceted representation of user-item characteristics
Innovation
Methods, ideas, or system contributions that make the work stand out.
Generates multiple distinct user and item profiles
Extrapolates profiles into semantic vectors in language space
Minimizes differences across environments to reveal preference invariance
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